Abstract
Abstract Neuroblastoma (NB) cells exhibit a complex spectrum of pathway changes associated with oncogene activation, chromosome events, tumor micro-environment and super-enhancer states. So far, elucidating which pharmaceutical compounds could modulate the activation level of each known pathway in NB cells has not been feasible. To solve this problem, we have combined transcriptome profiling of drug-perturbed NB cells with mathematical modeling of public data to create a first map of drug- and NB-specific transcriptional signatures for more than 7000 pharmaceutical compounds. We treated 2 patient-derived xenograft (PDX) NB cell lines with different chemical compounds, at 3 different doses (IC50, IC20, and IC10) and 2 time-points (6 and 24h). The whole-transcriptome of the treated cells was analyzed by a cost-effective hybrid ‘PLATE-Seq/SMART-Seq2’ method. Using factor analysis and supervised machine learning, the data was used to build a model that predicts the NB-specific outcome of 7035 drugs studied in other (non-NB) cell lines in the L1000/LINCS project. Analysis of 768 NB-specific transcriptome profiles, showed that our method accurately detects dose-dependent drug-induced pathway changes in the NB cells, including alteration of characteristic pathways and risk signatures. From the 768 profiles, our best-fitted machine learning model was then extended to 7035 compounds by in silico extrapolation, providing a first panel of estimated drug effects in NB cells. Our constructed NB pharmacological map provides a detailed view of both predicted and in vitro-validated drug-NB interactions. Effective treatments for high-risk NB are currently lacking. Using a new and cost-effective strategy, we could build an accurate map of drug-induced pathway alterations, for network pharmacology and drug repositioning for NB patients. Citation Format: Ramy Elgendy, Elin Almstedt, Michael Vanlandewijck, Sven Nelander. The impact of drug perturbations on neuroblastoma disease pathways: A pharmaco-transcriptomics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2949.
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